Adaptive online performance evaluation of video trackers

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. SanMiguel, A. Caballaro, and J. M. Martínez, "Adaptive Online Performance Evaluation of Video Trackers", IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2812 - 2823. May 2012We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries.This work was partially supported by the Spanish Government (TEC2007- 65400 SemanticVideo), Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, Consejería de Educación of the Comunidad de Madrid and European Social Fund

Visual object tracking performance measures revisited

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased towards particular tracking aspects. In this paper we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing towards homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent Visual Object Tracking (VOT) challenges as the foundation for the evaluation methodology

Prédiction sélective des traitements pour le suivi d'objet

International audienceL'un des problèmes majeurs du suivi d'objet en vidéo est la dérive, c'est-à-dire la mauvaise localisation à un instant donné d'une entité désignée dans une image initiale. Les nombreux algorithmes de suivi visuel ou trackers sont tous susceptibles de dériver lorsqu'ils sont confrontés à diverses sources de nuisance (occultations, changements d'apparence ou d'illumination, mouvements irréguliers de la cible et de la caméra...) mais sont également caracté-risés par des niveaux de robustesse variés à ces phéno-mènes. L'approche proposée par notre travail est de s'ins-pirer d'une caractéristique de la vision naturelle, l'atten-tion visuelle, considérée ici comme un ensemble de mé-canismes prédictifs de sélection de l'information ou des ressources. Dans le problème étudié, son rôle est alors de combiner dynamiquement les trackers selon leur capacité à assurer globalement le suivi à partir de mesures d'auto-évaluation en ligne de leur comportement et de leur adé-quation au contexte. L'effort est porté sur la combinaison de trackers hétérogènes en coût de calcul et en structure fonctionnelle, et sur la capacité de les munir générique-ment de telles fonctions d'auto-évaluation. Mots Clef attention visuelle, vision par ordinateur, suivi d'objet, pré-diction de dérives, fusion Abstract One of the key problems in video object tracking is drift, this is the bad localization of an entity initially appointed in an image at a given moment. The numerous visual tracking algorithms or trackers are all likely to drift when they are confronted with diverse sources of disturbances (occlusions, appearance changes or illuminations, irregular movements of the target and the camera) but are also characterized by different levels of robustness in response to these phenomena. The approach proposed by our work is inspired by a characteristic of natural vision, visual attention , considered here as a set of predictive mechanisms of selection of the information or the resources. In the studied problem, its role is then to dynamically combine trackers according to their capacity to globally insure tracking from measures of on-line self-assessment of their behaviour and their adequacy to the context. In our combination of heterogeneous trackers, the efforts concern the cost of calculation , the functional structure, and the capacity to equip them generically with such self-assessment functions